Design and specify internal services for AI agents and the tooling that powers the organization’s internal AI transformation. This is primarily a technical/systems analysis role — translating business needs into clear specifications for AI-agent services, internal automation tools, and AI-enabled workflows — combined with a readiness to take on project coordination and delivery-management tasks when needed. The role also involves owning multiple parallel initiatives across different teams, ensuring alignment with business goals, timelines, and technical constraints.
Node.js, Nest.js, JS/TS, TypeORM, PostgreSQL, AWS, React, Next.js, SCSS, Java, plus LLM/agent frameworks (e.g. MCP), prompt/agent orchestration tools, vector databases, LangChain or other agent SDKs, internal automation and workflow platforms, AI coding assistants (Claude Code, Cursor, etc.)
•Translate business and product needs into system specifications for AI-agent services and internal tooling;
•Design end-to-end logic for AI-agent services, including integrations with internal systems and third-party AI/LLM platforms;
•Define API interactions, data structures, and integration requirements for AI-agent and automation services;
•Document workflows, data flows, and system behavior using structured methodologies and diagramming tools;
•Support engineering teams during implementation; clarify requirements and validate delivered functionality against intent, ensuring system behavior matches business requirements and defined specifications;
•Analyze existing internal processes and propose where AI agents/automation can increase efficiency, reliability, and scalability;
•Assist in defining data requirements and basic SQL/analytical queries to support reporting on AI initiative outcomes;
•Plan and track delivery of AI-agent services and AI-transformation initiatives; manage timelines, dependencies, and risks;
•Own multiple parallel initiatives across different teams, ensuring alignment with business goals, timelines, and technical constraints;
•Manage several concurrent workstreams (features, integrations, internal tools), maintaining clear prioritization, dependency tracking, and risk visibility;
•Coordinate cross-functional efforts between engineering, product, data, and operations to roll out AI tools internally, acting as a coordination point that consolidates stakeholder inputs into structured, actionable requirements;
•Decompose high-level business needs into well-defined artifacts: system specifications, process flows, API contracts, and task breakdowns for engineering teams;
•Monitor execution and deadlines across streams, proactively identifying bottlenecks, resolving blockers, and adjusting plans to maintain delivery commitments;
•Implement structured tracking of initiatives (roadmaps, timelines, dependencies, risks), ensuring transparency and predictab